System for supporting user&#39;s behavior

ABSTRACT

Provided is a system  10  which supports a user&#39;s behavior by generating a behavioral decision function indicating behavior to be adopted to a certain target. The system  10  includes: a data acquiring section  110  which acquires a cost caused as a result of adopting each of a plurality of behaviors to a target as training data for generating the behavioral decision function, the plurality of behaviors having already been adopted to the target; and a function generator  120  which generates, based on the training data, the behavioral decision function to minimize the expected shortfall of a cost to be obtained as a result of adopting the behavior to the target.

BACKGROUND OF THE INVENTION

The present invention relates to a system for supporting a user'sbehavior, and particularly relates to a system which supports a user'sbehavior by generating a behavioral decision function.

Classification learning is studied as a basic technique of data mining.An object of the classification learning is to output behavior on acertain target, which should be adopted in the future, based oninformation showing the result of behavior which was adopted to thetarget in the past (hereinafter, referred to as training data). If thistechnique is applied, according to the past events, it is possible tosuggest the most statistically appropriate (e.g., the number of errorsis minimized) behavior to a user to support a user's behavior.

The classification learning can be applied to various technical fieldsas follows:

(1) Diagnosis in the Medical Field

-   Target: test result of patient-   Behavior: whether or not a certain treatment should be performed

Training data in this example is information showing whether or not acertain treatment was successful when the treatment was performed in thepast on a patient having a certain test result. According to theclassification learning, it is possible to predict the appropriatenessof a treatment on a future patient based on such training data.

(2) Credit Assessment in the Financial Field

-   Target: credit history of applicant for loan-   Behavior: whether or not a loan is granted

Training data in this example is information showing whether or not abond was collectible when a loan was made in the past for an applicanthaving a certain credit history. According to the classificationlearning, it is possible to judge whether or not to finance a certainapplicant in the future based on such training data.

(3) Topic Classification in a Search Engine

-   Target: webpages of news-   Behavior: classification into economic, sport, and political fields

Training data in this example is information showing whether or not theclassification was appropriate when a certain webpage was classifiedinto a certain field in the past. According to the classificationlearning, a webpage which will be created in the future can beclassified appropriately based on such training data.

In general, an object of such classification learning is to accuratelypredict behavior to be adopted to a target. In other words, theclassification learning aims to minimize the number and probability oferrors in behaviors.

However, minimizing the number of errors alone may not be sufficient insome problems. For example, in the case of the above example (1), thereis a clear difference between a loss (hereinafter, referred to as acost) caused as a result of diagnosing a healthy patient with a diseaseand then performing an unnecessary treatment and a cost caused as aresult of leaving a sick patient alone and then leading to his/herdeath. Moreover, there may be a case where a cost is different accordingto the social status of a patient. Similarly, in the case of the aboveexample (2), a cost caused as a result of refusing a loan to anexcellent applicant is an interest alone. However, a cost caused as aresult of granting a loan to a bad applicant may be the entire amount ofthe loan. The cost is different also in this case according to therespective amount of his/her loan and degree of his/her badness.

As an applicable technique in such a case where costs are differentamong each target and behavior and they are unknown when prediction ismade, cost-sensitive learning has conventionally been proposed (pleaserefer to: N. Abe and B. Zadrozny. An interactive method for multi-classcost-sensitive learning. In Proceedings of ACM SIGKDD Conference, 2004.;J. P. Bradford, C. Kunz, R. Kohavi, C. Brunk, and C. E. Brodley. Pruningdecision trees with misclassification costs. In Proceedings of the9^(th) European Conference on Machine Learning (ECML), 1998.; P.Domingos. MetaCost: A general method for making classifier costsensitive. In Proceedings of the 5^(th) International Conference onKnowledge Discovery and Data Mining, pages 155-164, 1999.; C. Elkan. Thefoundations of cost-sensitive learning. In Proceedings of the 17^(th)International Joint Conference on Artificial Intelligence (IJCAI), pages973-978, 2001.; W. Fan, S. J. Stolfo, J. Zhang, and P. K. Chan.Ada-Cost: Misclassification cost sensitive boosting. In Proceedings ofthe 16^(th) International Conference on Machine Learning (ICML), pages97-105, 1999.; P. Geibel, U. Bredford, and F. Wysotzki. Perceptron andSVM learning with generalized cost models. Intelligent Data Analysis,8(5):439-455, 2004.; B. Zadrozny and C. Elkan. Learning and makingdecisions when costs and probabilities are both unknown. In Proceedingsof ACM SIGKDD Conference, 2001.; B. Zadrozny, J. Langford, and N. Abe.Cost-sensitive learning by cost-proportionate example weighting. InProceedings of the 3^(rd) International Conference on Data Mining(ICDM), pages 435-442, 2003.; and Suzuki. More Advantageous Learningthan Accurate Learning—Classification Learning ConsideringMisclassification Costs—(1) (2). Information Processing, 45(4-5),2004.). An object of the cost-sensitive learning is not to minimize therate of behavioral errors, but to minimize the expected value of a cost.Therefore, it is possible to handle problems in a wider range.

Hereinafter, more detailed descriptions will be given of thecost-sensitive learning. Firstly, problems targeted in thecost-sensitive learning will be defined by the following (1) to (3).

(1) Cost Function

A cost means an indicator which shows a loss caused as a result ofbehavior adopted to a certain target, for example. Assume that X is aset of targets (for example, X=R^(M)) and that Y is a set of behaviorswhich can be adopted to the targets. It should be noted that Y isassumed to be a discrete and finite set. A cost caused as a result ofadopting behavior y∈Y on a target x∈X is assumed to be c(x, y)∈R.

For example, the badness of a result caused when a certain treatment yis performed on a patient having a test result x is c(x, y). If thetreatment is appropriate, c(x, y) is small. If the treatment isinappropriate, c(x, y) is large. If the treatment y is extremelyinappropriate as a treatment for the patient and leads to his/her death,the cost becomes very large. Incidentally, in a problem setting (pleaserefer to J. P. Bradford, C. Kunz, R. Kohavi, C. Brunk, and C. E.Brodley. Pruning decision trees with misclassification costs. InProceedings of the 9^(th) European Conference on Machine Learning(ECML), 1998.) in the early study stage, handled is a simple case where:the cost does not depend on x directly; classes are set as latentvariables; the cost depends on the class and the behavior; and moreover,the scale of the cost is already known. Here, handled is a more commoncase where costs are different according to targets and a real costfunction c (x, y) is unknown (please refer to B. Zadrozny and C. Elkan.Learning and making decisions when costs and probabilities are bothunknown. In Proceedings of ACM SIGKDD Conference, 2001.).

(2) Behavior Decision Model

X is assumed to be a set of targets (for example, X=R^(M)), and Y isassumed to be a (discrete and finite) set of behaviors which can beadopted for the targets. A function used for deciding the behavior y∈Yto the target x∈X is assumed to be the following equation (1).

(Equation 1)h(x,y;θ):X×Y→R   Equation 1

Here, θ is a parameter of the model. In general, using this, behavior y′which should be adopted is alternatively decided by the followingequation 2. h(x, y; θ) may have a probabilistic constraint as in thefollowing equation 3.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 2} \right) & \; \\{y^{\prime} = {\underset{y \in Y}{argmax}\mspace{11mu}{h\left( {x,{y;\theta}} \right)}}} & {{Equation}\mspace{14mu} 2} \\\left( {{Equation}\mspace{14mu} 3} \right) & \; \\{{\sum\limits_{y \in Y}{h\left( {x,{y;\theta}} \right)}} = {{1\mspace{14mu}{s.t.{h\left( {x,{y;\theta}} \right)}}} \geq 0}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In other words, when the target x∈X is given, behavior decision on thismay probabilistically be made by equation 3 instead of equation 2.Furthermore, it is also conceivable that the behavior decision is aresource distribution type, that is, it is a case where the number ofbehaviors which can actually be adopted is not one but a diversifiedinvestment can be made in h (x, y; θ) in terms of the resource inaccordance with the proportion thereof. However, behavior isalternatively decided by equation 2 in the embodiment of the presentinvention.

In addition, c(x, h, (θ)) is assumed to be a cost caused when behavioron x is decided by using h (x, y; θ). In the case (1) of an alternativeaction, c(x, h(θ)) is described in the following equation 4.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 4} \right) & \; \\{{c\left( {x,{h(\theta)}} \right)} = {c\underset{y \in Y}{\left( {x,{{argmax}\mspace{14mu}{h\left( {x,{y;\theta}} \right)}}} \right)}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In a case of a diversified-investment typed action, the definition isnot necessarily obvious. However, here, as a simpler case, c(x, h, (θ))is assumed, as shown in equation 5, that a cost produced by each actionis proportional to an investment amount.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 5} \right) & \; \\{{c\left( {x,{h(\theta)}} \right)} = {\sum\limits_{y \in Y}{{h\left( {x,{y;\theta}} \right)}{c\left( {x,y} \right)}}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$(3) Training Data

A target and a cost are considered to be uniformly generated from aprobability distribution D defined by X×R^(Y), and a set E of N piecesof data which have been sampled from D is assumed to be given. Here, thei-th training data of E is assumed to be e^((i))=(x^((i)),{c^((i))(x^((i)), y)}y∈Y). x^((i))∈X is assumed to be the i-th target ofthe training data, and the cost c^((i))(x^((i)), y) is assumed to begiven to each action y∈Y on the i-th target.

With regard to the above problems, conventionally, used is a methodwhose object is to minimize the expected value of a cost in aclassification problem which requires a consideration into a cost.Specifically, although θ is desired to be decided in a manner ofminimizing an expected cost (equation 6) with respect to thedistribution D of the data, since the distribution D is actuallyunknown, the parameter θ is to be decided in a manner of minimizing anexperienced expectation cost (equation 7) (please refer to N. Abe and B.Zadrozny. An interactive method for multi-class cost-sensitive learning.In Proceedings of ACM SIGKDD Conference, 2004., P. Geibel, U. Bredford,and F. Wysotzki. Perceptron and SVM learning with generalized costmodels. Intelligent Data Analysis, 8(5):439-455, 2004., and B. Zadroznyand C. Elkan. Learning and making decisions when costs and probabilitiesare both unknown. In Proceedings of ACM SIGKDD Conference, 2001.)

$\begin{matrix}\left( {{Equation}\mspace{14mu} 6} \right) & \; \\{{C^{D}(\theta)} = {E_{D}\left\lbrack {c\left( {x,{h(\theta)}} \right)} \right\rbrack}} & {{Equation}\mspace{14mu} 6} \\\left( {{Equation}\mspace{14mu} 7} \right) & \; \\{{C^{E}(\theta)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}{c\left( {x^{(i)},{h(\theta)}} \right)}}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

It should be noted that it is considered that the target and the costare generated from the probability distribution D which is defined byX×R^(Y), independently of each other. The set E of N pieces of the data,which has been sampled from D, is assumed to be given as the trainingdata. Here, the i-th training data of E is assumed to be training datae^((i))=(x^((i)), {c^((i))(x^((i)), y)}y∈Y). x^((i))∈X is assumed to bethe i-th target of the training data and a cost c^((i))(x^((i)), y) ofwhen adopting each behavior y∈Y is assumed to be given.

However, considering from a viewpoint of a risk management, an approachof simply minimizing an experienced expectation cost may not besufficient. After the training, behavior is assumed to be adopted for Mpieces of data. When M is large, a sum of their costs comes close toM·C^(D)(θ). Hence, it seems that there is no problem in setting C^(E)(θ)as an objective function of learning. However, since M is relativelysmall, the above approximation cannot hold true. Additionally,consideration is given to a case where the generation of a large amountof cost is critical. For example, in a case of a problem of decidingwhere to invest a fund, a fact that big mistakes occur consecutivelysome times is a serious problem which is directly connected to the riskof bankruptcy. When the probability of the occurrence is small but thereis a possibility that a large amount of cost to an unacceptable degreeoccurs, a user should wish to avoid its risk as much as possible.

Furthermore, for example, assume that there are two decision functionsh1 and h2 which can be expected to obtain the same cost expected values.Although a probability distribution of a cost brought by h1 has a highpeak around the expected value, a probability distribution of a costbrought by h2 has a form which has gentle slopes and whose bottom sideis wide in a high cost area. In this case, even if the expectation costis the same, it is presumed that preferred is h1 whose possibility ofthe occurrence of a high cost is smaller. In such a case, it cannot besaid that the object is correctly reflected by the minimization of anexperienced expectation cost. Therefore, desired is a learning method inwhich a risk is avoided more actively, taking the distribution of a costinto consideration.

SUMMARY OF THE INVENTION

Hence, an object of the present invention is to provide a system, amethod, and a program, which can solve the above problems. The object isachieved by combining the features recited in independent claims in thescope of claims. Additionally, dependent claims stipulate moreadvantageous, specific examples of the present invention.

In order to solve the above problems, in the embodiment of the presentinvention, provided are a system which supports the behavior of a userby generating a behavioral decision function showing behavior to beadopted to a certain target, including: a data acquiring section whichacquires a cost caused as a result of adopting the behavior to thetarget as training data for generating a behavioral decision function,for each of a plurality of behaviors already adopted to the target; anda function generator which generates, based on the training data, abehavioral decision function to minimize the expected shortfall of thecost obtained as a result of adopting the behavior to the target, aprogram causing an information processor to function as the system, anda method which supports the behavior of a user with the system.

It should be noted that the above summary of the invention does not citeall the necessary features of the present invention, and that thesubcombination of the groups of these features can be the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention and theadvantage thereof, reference is now made to the following descriptiontaken in conjunction with the accompanying drawings.

FIG. 1 shows a functional configuration of a behavior supporting system10.

FIG. 2 shows an example of a data structure of a training data DB 100.

FIG. 3 shows a functional configuration of a function generator 120.

FIG. 4 shows a flowchart of processes that the behavior supportingsystem 10 supports a decision on a user's behavior.

FIG. 5 shows the details of the processes of calculating a behavioraldecision function in S410.

FIG. 6 shows a result of performing a test by the behavior supportingsystem 10 according to an embodiment.

FIG. 7 shows an example of hardware configuration of an informationprocessor 700 which functions as the behavior supporting system 10.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Hereinafter, descriptions will be given of the present invention throughan embodiment of the invention. However, the embodiment below is notintended to limit the invention to the scope of claims, and all thecombinations of features described in the embodiment are not necessarilyessential to the solving means of the invention.

FIG. 1 shows a functional configuration of a behavior supporting system10. The behavior supporting system 10 aims to support a decision onbehavior to be adopted to a certain target by a user. As an example, thebehavior supporting system 10 aims to support a doctor to make adecision on a treatment policy for a patient having a certain testresult.

The behavior supporting system 10 includes a training data DB 100, adata acquiring section 110, a function generator 120, and a behaviordecider 130. The training data DB 100 stores training data for causingthe behavior supporting system 10 to generate a behavioral decisionfunction. The training data, for example, shows a cost caused as aresult of adopting a certain behavior on its target for each of aplurality of behaviors already adopted to the target. The training datamay be generated based on the history of past behaviors on a certaintarget, or may be generated based on the result of each type ofsimulations and tests. In an example of supporting a decision on amedical policy, the training data is data showing the scale of a losscaused on a relevant patient for a plurality of patients who havealready received treatment.

The data acquiring section 110 provides training data for the functiongenerator 120 by acquiring the training data from the training data DB100. The function generator 120 generates a behavioral decision functionto minimize the expected shortfall of a cost obtained as a result ofadopting behavior to a certain target, based on the training data. Inthe example of supporting a decision on a medical policy, a behavioraldecision function is a function which decides a treatment policy forpatients. In addition, the behavioral decision function is generated ina manner of minimizing the expected shortfall of a cost caused by atreatment. This function may uniquely output one treatment policy when acertain target is given, or may output an index value showing a degreeof the appropriateness of each of the plurality of treatment policies.

When a target of a certain behavior is given, the behavior decider 130decides behavior to be adopted to the target based on a behavioraldecision function generated by the function generator 120, and notifiesa user of the above. Accordingly, the user can know behavior to minimizean index value that conforms to an actual event, that is, expectedshortfall, prior to a decision on the behavior. Consequently, the futurerisks can be reduced. In other words, specifically, a doctor can reducethe risk of leading an important patient to death due to a medicalerror, a financer can reduce the amount of a loan loss, and an investorcan reduce the risk of bankruptcy.

Moreover, the expected shortfall of a cost on a certain target is shownas a value-at-risk (hereinafter, referred to as VaR and please refer toT. C. Cormen, C. E. Leiserson, and R. L. Rivest. Introduction toAlgorithms. MIT Press, Cambridge, Mass., 1990.) of a cost which can beobtained as a result of adopting behavior to its target and a functionof a parameter θ of a behavioral decision function. Furthermore, in theembodiment, an index values which shows the upper bound of expectedshortfall is adopted. This index value is shown as a function which isconvex downward with regard to the VaR and the parameter θ. The functiongenerator 120 of the embodiment firstly calculates the parameter θ tominimize the index value by fixing the VaR. Secondly, the functiongenerator 120 calculates the VaR to minimize the index value by fixingthe parameter θ. The function generator 120 repeats these calculationsuntil the expected shortfall converges. In this manner, the behaviorsupporting system 10 of the embodiment can calculate an appropriatebehavioral decision function rapidly by setting a risk indicator to beminimized as expected shortfall and by minimizing the index value whoseupper bound is shown by the convex function.

FIG. 2 shows an example of a data structure of the training data DB 100.The training data DB 100 stores a loss caused as a result of treating apatient, as training data. In the example of FIG. 2, a behavioral targetcorresponds to a test result, behavior corresponds to a treatmentpolicy, and a loss corresponds to a drawback caused by a treatment.According to the training data stored in the training data DB 100, aloss is 10 in a case where a treatment is performed on a certain patienthaving a test result A with a treatment policy 1, and a loss is 6 in acase where a treatment is performed on the patient with a treatmentpolicy 2. On the other hand, a loss is 1 in a case where a treatment isperformed on another patient having the same test result A with thetreatment policy 1, and a loss is 5 in a case where a treatment isperformed on the patient with the treatment policy 2.

Although the loss is shown by being normalized with 1 to 10, aphenomenon shown as a result of a treatment, in reality, is shown bybeing converted into numbers, for example. In other words, for example,10 shows a state of death or in conformance with death, and 1 shows astate of the appearance of the adverse effects of medication. Instead ofthis, the loss may be something that a phenomenon appearing as a resultof a treatment is replaced with a monetary value.

Here, a mean loss of when a treatment is performed on the patient havingthe test result A with the treatment policy 1 is 5.5, and also a meanloss of when a treatment is performed with the treatment policy 2 is5.5. In this manner, although the mean losses in both cases of thetreatment policies 1 and 2 are 5.5, the maximum loss of the treatmentpolicy 1 is 10 and the maximum loss of the treatment policy 2 is 6.Since a loss equivalent to death should be avoided, it can be judged inthis example that the treatment policy 2, which has the smaller maximumloss, should be adopted even if the mean losses are the same. It isoften difficult to make such a judgment in more complicated cases.However, it is possible to make such a judgment appropriately in theembodiment, by setting an indicator which should be minimized to beexpected shortfall.

Similarly, a loss caused as a result of treating a certain patienthaving a test result B with the treatment policy 1 is 8, and a losscaused as a result of treating the patient with the treatment policy 2is 6. On the other hands, a loss caused as a result of treating anotherpatient having the same test result B with the treatment policy 1 is 7,and a loss caused as a result of treating the patient with the treatmentpolicy 2 is 7.

As described above and shown in FIG. 2, the training data DB 100 storesa cost caused as a result of adopting a certain behavior to a certaintarget. The cost may be decided based on the history of the results ofadopting actual behaviors in the past to the target, or may be decidedbased on the result of each type of test or simulations.

FIG. 3 shows a functional configuration of the function generator 120.The function generator 120 includes a first calculator 300, a thirdcalculator 305, a second calculator 310, and a convergence judgmentsection 330. When a given value is set to be the VaR of a cost, thefirst calculator 300 calculates a behavioral decision function tominimize an index value based on the total amount of costs which exceedsthe VaR, and stores the behavioral decision function in a memory. Theindex value, for example, is a predetermined value showing the upperbound of expected shortfall, and also a behavioral decision function ofwhen minimizing the index value is known in advance to minimize theexpected shortfall.

The third calculator 305 calculates a behavioral decision function tominimize the expected value of a cost based on training data. Atechnique to efficiently calculate the behavioral decision function tominimize the expected value of the cost is called a cost-sensitivealgorithm. A method of realizing this technique is, for example,described in N. Abe and B. Zadrozny. An interactive method formulti-class cost-sensitive learning. In Proceedings of ACM SIGKDDConference, 2004., P. Geibel, U. Bredford, and F. Wysotzki. Perceptronand SVM learning with generalized cost models. Intelligent DataAnalysis, 8(5):439-455, 2004. and B. Zadrozny, J. Langford, and N. Abe.Cost-sensitive learning by cost-proportionate example weighting. InProceedings of the 3^(rd) International Conference on Data Mining(ICDM), pages 435-442, 2003., and is conventionally and publicly known.Therefore, the descriptions will be omitted. The first calculator 300subtracts the given VaR from costs which correspond respectively tobehaviors included in the training data, and provides the result as newtraining data for the third calculator 305. Then, the first calculator300 causes the third calculator 305 to calculate the behavioral decisionfunction to minimize the expected value of a cost in the new trainingdata. The calculated behavioral decision function becomes a behavioraldecision function to minimize expected shortfall in a case where thegiven value is set to be a VaR.

The second calculator 310 reads the behavioral decision functioncalculated by the first calculator 300 from the memory, calculates theVaR of a cost caused as a result of adopting behavior indicated by thebehavioral decision function based on the training data, and providesthe result for the first calculator 300. The convergence judgmentsection 330 judges whether or not expected shortfall calculated based onthe index value minimized by the first calculator 300 and the VaRcalculated by the second calculator 310, have converged to a valuewithin a predetermined range. The function generator 120 outputs thebehavioral decision function calculated by the first calculator 300 oncondition that the expected shortfall has converged.

In this manner, the function generator 120 alternatively calculatesbehavioral decision functions and VaRs which make the expected shortfallsmaller, in order to cause the expected shortfall to come close to aminimum value. Then, a behavioral decision function at the point isoutputted on condition that the expected shortfall has converged. Atthis point, since the upper bound of the expected shortfall is shown asa function which is convex downward with regard to the parameter θ and aVaR, a parameter θ and a VaR to minimize the expected shortfall, can becalculated with an algorithm which is greedy for θ and a VaR. Due tothis, the expected shortfall approaches the minimum value everytime thebehavioral decision function is calculated. Thus, it is made possible tocalculate a target behavioral decision function efficiently.Furthermore, with regard to the calculation of a behavioral decisionfunction, it is possible to utilize an existing technique to calculatethe expected value of a cost in the third calculator 305, thus making itpossible to make the process efficient by use of the accumulation ofexisting techniques.

Hereinafter, by use of FIGS. 4 and 5, descriptions will be given of theflow of the processes of the embodiment. Prior to the descriptions ofthe flow of the processes, descriptions will firstly be given of theprocess of leading an algorithm which calculates a behavioral decisionfunction to minimize expected shortfall. The algorithm generates abehavioral decision function by calculating the parameter θ of thebehavioral decision function. In other words, the behavioral decisionfunction outputs the degree of appropriateness of the behavior byconverting the degree into numbers while setting a target x, behavior y,and the parameter θ to be input, and the parameter to decide the natureof the behavioral decision function is set at θ.

By using the parameter θ, expected shortfall is described as thefollowing equation 8. It should be noted that, in equation 8, [x]⁺ isassumed to be a function to return x when x is positive and 0 otherwise.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 8} \right) & \; \\{{\phi_{\beta}^{E}(\theta)} = {{\alpha_{\beta}^{D}(\theta)} + {\frac{1}{1 - \beta}{E_{D}\left\lbrack {{c\left( {x,{h(\theta)}} \right)} - {\alpha_{\beta}^{D}(\theta)}} \right\rbrack}^{+}}}} & {{Equation}\mspace{14mu} 8}\end{matrix}$

In this equation, the distribution D of a cost is unknown. Hence, adistribution E of the training data is used instead of the distributionD. Due to this, equation 8 can be rewritten as in equation 9. It shouldbe noted that the target of behavior in the training data is denoted byx⁽¹⁾ to x^((n)) in equation 9. In addition, a behavioral decisionfunction is denoted by a function h for deciding the behavior y to beadopted in accordance with the parameter θ while setting the target x tobe input. Furthermore, a cost in the training data is denoted byc(x^((i)), h(θ)). Moreover, a probability of the generation of a costexceeding a VaR is set at a constant β.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 9} \right) & \; \\{{\phi_{\beta}^{E}(\theta)} = {{\alpha_{\beta}^{E}(\theta)} + {\frac{1}{\left( {1 - \beta} \right)N}{\sum\limits_{i - 1}^{N}\left\lbrack {{c\left( {x^{(i)},{h(\theta)}} \right)} - {\alpha_{\beta}^{E}(\theta)}} \right\rbrack^{+}}}}} & {{Equation}\mspace{14mu} 9}\end{matrix}$

Here, α^(E) _(β)(θ) is a VaR with respect to the distribution E of thetraining data, and is described by equation 10. It should be noted thata function I is assumed to be a function which adopts 1 when a conditionshown by an argument is satisfied, and 0 when the condition is notsatisfied.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 10} \right) & \; \\{{\alpha_{\beta}^{E}(\theta)} = {{\min\left\{ {\alpha \in R} \right.\frac{1}{N}{\sum\limits_{i - 1}^{N}\left\lbrack {I\left( {{c\left( {x^{(i)},{h(\theta)}} \right)} \geq \alpha} \right)} \right\rbrack}} \leq {1 - \beta}}} & {{Equation}\mspace{14mu} 10}\end{matrix}$

Incidentally, given that α^(E) _(β)(θ) is an already-known constant α′in equation 9, it is sufficient if only the following equation 11included in the second term of equation 9 is minimized.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 11} \right) & \; \\{{C_{\alpha^{\prime}}^{E}(\theta)}:={\frac{1}{N}{\sum\limits_{i = 1}^{N}\left\lbrack {{c\left( {x^{(i)},{h(\theta)}} \right)} - \alpha^{\prime}} \right\rbrack^{+}}}} & {{Equation}\mspace{14mu} 11}\end{matrix}$

It should be noted that a function [x]⁺ is a convex function which isnot reduced with respect to x. Therefore, if c(x^((i)), h(θ)) is convexwith respect to θ, equation 11, too, becomes convex likewise.Descriptions will later be given of an algorithm which minimizesequation 11.

Next, with regard to the given parameter θ, a VaR with respect to this θis defined for the training data. Hence, the parameter θ can bedescribed as in the following equations 12 and 13.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 12} \right) & \; \\{{\alpha_{\beta}^{E}(\theta)} = {C\left( {x^{(k^{'})},{h(\theta)}} \right)}} & {{Equation}\mspace{14mu} 12} \\\left( {{Equation}\mspace{14mu} 13} \right) & \; \\{k^{\prime} = {{\min\left\{ k \right.\frac{1}{N}{\sum\limits_{i = 1}^{N}\left\lbrack {I\left( {{c\left( {x^{(i)},{h(\theta)}} \right)} \geq {c\left( {x^{(k)},{h(\theta)}} \right)}} \right)} \right\rbrack}} \leq {1 - \beta}}} & {{Equation}\mspace{14mu} 13}\end{matrix}$

This equals to the (1−β)·N-th (round down) largest one (c(x^((k)), h(θ))among costs generated by θ. Therefore, it is possible to find this bythe algorithm obtaining an order statistic in O (N) period of time(please refer to T. C. Cormen, C. E. Leiserson, and R. L. Rivest.Introduction to Algorithms. MIT Press, Cambridge, Mass., 1990., forexample).

Based on the above derivation, descriptions will hereinafter be given ofthe processes of calculating a behavioral decision function to minimizeexpected shortfall by use of FIG. 4.

FIG. 4 shows a flowchart of the processes that the behavior supportingsystem 10 supports a decision on the behavior of a user. The dataacquiring section 110 acquires training data from the training data DB100 (S400). Based on the training data, the function generator 120performs the following processes. Firstly, when a given value is set tobe the VaR of a cost, the first calculator 300 calculates, based on thetraining data, a behavioral decision function to minimize an index valuebased on the total amount of costs which exceed the VaR (S410). Thegiven value is an initial value given in advance (for example, 0) in thefirst calculation, and a VaR given by the second calculator 310 insubsequent calculations. In details, the first calculator 300 calculatesa parameter θ′ of a behavioral decision function to minimize an indexvalue C^(E) _(α)(θ) which is calculated by the above equation 11 whilesetting the given VaR at α′. The value of the parameter θ′ is set at anew value of θ.

Next, the second calculator 310 calculates, based on the training data,a VaR of a cost caused as a result of adopting behavior shown by abehavioral decision function which is calculated by the first calculator300 (S420). In details, the second calculator 310 calculates α^(E)_(β)(θ) which is calculated by the above equations 12 and 13, as a VaR,with respect to the calculated parameter θ. Then, the second calculator310 provides the value as α′ for the first calculator 300.

Next, the convergence judgment section 330 calculates an index valueF^(E) _(β)(θ, α) showing the upper bound of expected shortfall, based onthe index value C^(E) _(α′) minimized by the first calculator 300 and aVaR calculated by the second calculator 310 (S430). The index value, forexample, is a value that a value that the index value of C^(E) _(α′)minimized by the first calculator divided by a probability (1−β) that acost is equal to or below a value-at-risk, is added to the VaR.Additionally, the index value is shown by the following equation 14.

$\begin{matrix}\left( {{Equation}\mspace{14mu} 14} \right) & \; \\{{F_{\beta}^{E}\left( {\theta,\alpha} \right)} = {\alpha + {\frac{1}{\left( {1 - \beta} \right)N}{\sum\limits_{i = 1}^{N}\left\lbrack {{c\left( {x^{(i)},{h(\theta)}} \right)} - \alpha} \right\rbrack^{+}}}}} & {{Equation}\mspace{14mu} 14}\end{matrix}$

With regard to this equation 14, as shown in R. T. Rockafellar and S.Uryasev. Optimization of conditional value-at-risk. Journal of Risk,2(3):21-41, 2000., it is known that the following equation 15 issatisfied. In addition, equation 14 is convex with respect to α, andequation 7 is convex with respect to θ. Therefore, equation 14 is convexwith respect to θ and α. Furthermore, the following equation 16 holdstrue.

$\begin{matrix}{\left( {{Equation}\mspace{14mu} 15} \right){{\min\limits_{\theta}{\phi_{\beta}^{E}(\theta)}} = {\min\limits_{\theta,\alpha}{F_{\beta}^{E}\left( {\theta,\alpha} \right)}}}} & {{Equation}\mspace{11mu} 15} \\{\left( {{Equation}\mspace{14mu} 16} \right){\alpha_{\beta}^{E}(\theta)} = {\min\left\{ {\alpha \in {\underset{\alpha}{\arg\;{m{in}}}\;{F_{\beta}^{E}\left( {\theta,\alpha} \right)}}} \right\}}} & {{Equation}\mspace{14mu} 16}\end{matrix}$

As shown above, it can be seen that when the index value of equation 16converges to its minimum value, expected shortfall, too, converges toits minimum value. Moreover, the index value is shown by the functionsof a VaR and θ, and becomes a function which is convex downward, withrespect to the VaR and θ. Therefore, the convergence judgment section330 judges whether or not expected shortfall has converged, by judgingwhether or not the index value of equation 16 converges to a valuewithin a predetermined range (S440). It should be noted that since theVaR and θ, too, converge to predetermined values accompanied by theconvergence of the expected shortfall, the convergence judgment section330 may judge the convergence of expected shortfall by judging theconvergence of the VaR, or may judge the convergence of expectedshortfall by judging the convergence of θ.

If the expected shortfall has not converged (S440: NO), the functiongenerator 120 returns the process to S410. In this case, the firstcalculator 300 recalculates the parameter θ by use of the VaR calculatedin S420. On the other hand, on condition that the expected shortfall hasconverged (S440: YES), the function generator 120 outputs the parameterθ of a behavioral decision function calculated by the first calculator300 (S450). The behavior decider 130 supports a user's behavior based onthe behavioral decision function calculated by the above processes(S460).

FIG. 5 shows the details of the processes of calculating a behavioraldecision function in S410. In a case where behavior is alternativelydecided by a behavioral decision function, a cost is limited to a formof [C^((i))(x^((i)),y)−α′]⁺+α′. Considering the above equation 11becomes the expected value of a cost exceeding α′, equation 7 is shownas the following equation 18 by replacing the cost with an original costin the following equation 17. Since this has the same form as equation7, it is possible to minimize expected shortfall by providing a trainingexample in which a cost is changed in an existing example-dependentcost-sensitive algorithm as in equation 17.

$\begin{matrix}{\left( {{Equation}\mspace{14mu} 17} \right){{c^{\prime{(i)}}\left( {x^{(i)},y} \right)} = \left\lbrack {{c^{(i)}\left( {x^{(i)},y} \right)} - \alpha^{\prime}} \right\rbrack^{+}}} & {{Equation}\mspace{14mu} 17} \\{\left( {{Equation}\mspace{14mu} 18} \right){{C_{\alpha^{\prime}}^{\prime E}(\theta)} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\;{c^{\prime{(i)}}\left( {x^{(i)},y} \right)}}}}} & {{Equation}\mspace{14mu} 18}\end{matrix}$

Considering the above, the first calculator 300 performs the followingprocesses. Firstly, the first calculator 300 calculates a cost c′^((i))by subtracting the given value-at-risk α′ from a cost c^((i)) withrespect to the target x^((i)) and the behavior y in the training data byequation 17, and provides the result for the third calculator 305(S500). Then, the third calculator 305 calculates the parameter θ of abehavioral decision function to minimize a cost C′^(E) _(α′)(θ) shown byequation 18 (S510).

The cost C′^(E) _(α′)(θ) shown by this equation 18 becomes the expectedvalue of a cost found from the training data. An algorithm to calculatethe expected value of a cost is conventionally studied in N. Abe and B.Zadrozny. An interactive method for multi-class cost-sensitive learning.In Proceedings of ACM SIGKDD Conference, 2004., P. Geibel, U. Bredford,and F. Wysotzki. Perceptron and SVM learning with generalized costmodels. Intelligent Data Analysis, 8(5):439-455, 2004. and B. Zadrozny,J. Langford, and N. Abe. Cost-sensitive learning by cost-proportionateexample weighting. In Proceedings of the 3^(rd) International Conferenceon Data Mining (ICDM), pages 435-442, 2003., and the like, and itsefficient execution method is known. The first calculator 300 of theembodiment can lead a problem of minimizing expected shortfall to aproblem of minimizing the expected value of a cost, by subtracting agiven VaR from each cost included in the training data. Expectedshortfall can be thereby minimized efficiently by using an efficientalgorithm which is conventionally studied.

FIG. 6 shows a result of performing a test by the behavior supportingsystem 10 according to the embodiment. As a test result, German CreditData used in P. Geibel, U. Bredford, and F. Wysotzki. Perceptron and SVMlearning with generalized cost models. Intelligent Data Analysis,8(5):439-455, 2004., was used. A problem targeted by the test is aproblem to predict the credit risk of a customer, and a problem toclassify him/her into good or bad customers based on the customer'sinformation. The attribute of customer's information x is composed of 20items such as sex, job, the intended purpose of a fund, and a pasthistory.

In this test, all the attributes attached to a dataset were convertedinto 24 numeric attributes. There are behaviors of two types of whetheror not to make a loan. If a loan which should be made is not made, aloss equivalent to an interest occurs. On the other hand, a loan whichshould not be made is made, a loss equivalent to a large part of theloan amount occurs. Other conditions were in conformance with P. Geibel,U. Bredford, and F. Wysotzki. Perceptron and SVM learning withgeneralized cost models. Intelligent Data Analysis, 8(5):439-455, 2004.

In the test, a model h(x, y) of a case of alternatively selecting anaction and a weak hypothesis ft(x, y) of a case of adiversified-investment typed algorithm were used. In any case, used is akernel-based cost-sensitive perceptron (please refer to P. Geibel, U.Bredford, and F. Wysotzki. Perceptron and SVM learning with generalizedcost models. Intelligent Data Analysis, 8(5):439-455, 2004.) as acost-sensitive learning machine. The Gaussian kernel (σ=50) was used asa kernel function.

For the test result, a measurement was taken by use of the mean value ofvalues obtained by 3 fold cross validation on data (666 training dataand 334 test data). The column of Cost-Sensitive showed the resultsobtained by a conventional cost-sensitive perceptron in which anexpected cost is set to be an objective function, and the column ofRisk-Sensitive showed the results obtained by a proposal method in whichexpected shortfall is set to be an objective function (the respectivecases of β=0.20, 0.10, 0.05 and 0.01).

Each line shows the mean of three times of expected shortfall in each βin the test data. Moreover, the figures within parentheses showvalue-at-risks in the respective cases. A Mean Cost line in the bottomof the Cost-Sensitive column shows mean costs. As expected, it can beseen that the expected shortfall in corresponding β could be reduced inthe case of the cost-sensitive learning. The larger β becomes, the lessdramatic the amount of reduction in the expected shortfall becomes inthe risk-sensitive type. It is conceivable that this is because adifference between the expected shortfall and the expected costs becomessmaller since the cost distribution is heavily tilted toward the leftside (in the vicinity of 0). Additionally, in β=0.20, it can be seenthat a smaller value-at-risk was realized in the cost-sensitive typerather than the risk-sensitive type. This indicates that a smallvalue-at-risk does not necessarily suppress the large cost itself whichis caused with small probability.

FIG. 7 shows an example of a hardware configuration of an informationprocessor 700 functioning as the behavior supporting system 10. Theinformation processor 700 includes: a CPU periphery having a CPU 1000, aRAM 1020 and a graphic controller 1075, which are mutually connected bya host controller 1082; an input/output section having a communicationinterface 1030, a hard disc drive 1040, and a CD-ROM drive 1060, whichare connected to the host controller 1082 by an input/output controller1084; and a legacy input/output section having a BIOS 1010, a flexibledisc drive 1050, and an input/output chip 1070, which are connected tothe input/output controller 1084.

The host controller 1082 connects the RAM 1020 to the CPU 1000 and thegraphic controller 1075 which access the RAM 1020 at a high transferrate. The CPU 1000 is operated based on a program stored in the BIOS1010 and the RAM 1020, and controls each section. The graphic controller1075 acquires image data generated on a frame buffer which is providedby the CPU 1000 and the like in the RAM 1020, and displays the imagedata on a display device 1080. In stead of this, the graphic controller1075 may internally include the frame buffer which stores image datagenerated by the CPU 1000 and the like.

The input/output controller 1084 connects the host controller 1082 tothe communication interface 1030, the hard disc drive 1040, and theCD-ROM drive 1060, which are relatively high-speed input/output devices.The communication interface 1030 communicates with an external devicevia a network. The hard disc drive 1040 stores a program or data used bythe information processor 700. The CD-ROM drive 1060 reads the programor data from a CD-ROM drive 1095, and provides it for the RAM 1020 orthe hard disk drive 1040.

Furthermore, the input/output controller 1084 is connected to therelatively low-speed input/output devices such as the BIOS 1010, theflexible disc drive 1050 and the input/output chip 1070. The BIOS 1010stores: a boot program executed by the CPU 1000 upon the boot-up of theinformation processor 700; a program dependent on the hardware of theinformation processor 700; and the like. The flexible disk drive 1050reads a program or data from a flexible disk 1090, and provides it forthe RAM 1020 or the hard disk drive 1040 via the input/output chip 1070.The input/output chip 1070 connects each type of the input/outputdevices via the flexible disk 1090, and a parallel port, a serial port,a keyboard port, a mouse port, for example.

A program provided for the information processor 700 is stored in arecording medium such as the flexible disc 1090, the CD-ROM 1095, or anIC card, and is provided by a user. The program is read from therecording medium via the input/output chip 1070 and/or the input/outputcontroller 1084, and is executed by being installed in the informationprocessor 700. The operations to be performed by causing the program towork on the information processor 700 and the like are the same as thosein the behavior supporting system 10 described in FIGS. 1 to 6. Thus,the descriptions will be omitted. The program described above may bestored in an external recording medium. A recording medium can be: anoptical recording medium such as a DVD and a PD; a magneto-opticalmedium such as an MD; a tape medium; a semiconductor memory such as anIC card; and the like, in addition to the flexible disc 1090 and theCD-ROM 1095. Moreover, a recorder such as a hard disc or a RAM providedin a server system which is connected to a dedicated communicationsnetwork or the Internet may be used as a recording medium, and theprogram may be provided for the information processor 700 via thenetwork.

The technical scope of the present invention is not limited to the scopeas recited in the aforementioned embodiment, although the presentinvention has been described using the embodiment. It is obvious tothose skilled in this art that various modifications and improvementscan be added to the aforementioned embodiment. It is clearly understoodfrom description of the scope of claims that embodiments which areobtained by adding any of such various modifications and improvements tothe aforementioned embodiment are also included in the technical scopeof the present invention.

According to the present invention, it is possible to efficientlycalculate a behavioral decision function which instructs behavior forreducing the scale of a loss.

Although the preferred embodiment of the present invention has beendescribed in detail, it should be understood that various changes,substitutions and alternations can be made therein without departingfrom spirit and scope of the inventions as defined by the appendedclaims.

1. A method of generating a behavioral decision function by aninformation processing apparatus that enters a target and a behavior tobe adopted the target, and that generates the behavioral decisionfunction by quantifying a degree of proprietary to be adopted the targetand computing a parameter to define the behavioral decision function tobe outputted, comprising the steps of: data acquisition where theinformation processing apparatus acquires the target, behaviors, whichhave already been adopted with respect to the target, and a cost causedas a result of adopting the behaviors to the target as training data forgenerating the behavioral decision function; and function generationwhere the information processing apparatus generates the behavioraldecision function to minimize expected shortfall of the cost to beobtained as the result of adopting the behaviors to the target, based onthe training data, wherein the step of function generation comprises thesteps of: first calculation where the information processing apparatuscalculates the behavioral decision function by calculating the parameterto minimize an index value, which indicates an upper bound of anexpected shortfall based upon a sum of costs exceeding a value-at-riskin the training data, and which is convex downward with respect to theparameter, and that stores a calculated behavioral decision function ina memory, in a case that a provided value is the value-at-risk of thecost; second calculation where the information processing apparatusreads the behavioral decision function calculated by a first calculatorfrom the memory, and that calculates the value-at-risk of the costcaused as the result of adopting the behavior shown by the behavioraldecision function, based on the training data, thus providing the resultto the first calculator; and convergence judgment where the informationprocessing apparatus judges whether or not expected shortfall based onthe index value has been converged to a value within a predeterminedrange; and the parameter of the behavioral decision function calculatedby the first calculator is outputted, on condition that the expectedshortfall has been converged.